Tongliang Liu


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Tongliang Liu

Tongliang Liu

Director of Sydney AI Centre
Director of Trustworthy Machine Learning Lab (TML Lab)
School of Computer Science
Facult of Engineering
The University of Sydney

Visiting Scientist
Imperfect Information Learning Team
RIKEN AIP, Japan

Address: Room 315/J12/ 1 Cleveland St, Darlington, NSW 2008, Australia
E-mail: tongliang.liu [at] sydney.edu.au; tliang.liu [at] gmail.com
[Google Scholar] [DBLP]

 
Monograph on learning with noisy labels
by MIT Press. Coming in 2024!

Trustworthy Machine Learning Lab (TML Lab) at the University of Sydney hosts, attracts, and connects the best global talents to develop trustworthy machine learning techniques and tools, which are explainable, robust, fair, causally responsible, and privacy-preserving. Our mission is to make machines trustworthy, which is a foundation for our society to develop and deploy artificial intelligence to improve our lives. We are broadly interested in the fields of trustworthy machine learning and its interdisciplinary applications, with a particular emphasis on learning with noisy labels, adversarial learning, transfer learning, unsupervised learning, and statistical deep learning theory.

We are recruiting PhD and visitors. If you are interested, please send me your CV and transcripts.

We are always looking for highly-motivated undergraduate and postgraduate students to join our group. Full scholarships are available!

A few visiting positions in machine learning and computer vision are available.


Research Interests

My research interests lie in providing mathematical and theoretical foundations to justify and understand (deep) machine learning models and designing efficient learning algorithms for problems in computer vision and data mining, with a particular emphasis on
  • Learning with noisy labels

  • Deep adversarial learning

  • Causal representation learning

  • Deep transfer learning

  • Deep unsupervised learning

  • Statistical deep learning theory


Top News

  • 08/2022, I accepted the invitation to serve as an Area Chair for ICLR 2023.

  • 08/2022, Two of my PhD students have got Google PhD Fellowship Awards. Congrats Xiaobo and Shuo!

  • 07/2022, I accepted the invitation to serve as a Discussant for UAI 2022.

  • 04/2022, I served as a Session Chair for ICLR 2022.

  • 04/2022, I was selected as one of Global Top Young Chinese Scholars in AI by Baidu Scholar 2022.

  • 03/2022, I will co-organize IJCAI 2022 Challenge on Learning with Noisy Labels.

  • 03/2022, I accepted the invitation to serve as an Area Chair for NeurIPS 2022.

  • 02/2022, my student James Wood got the University Medal! Congrats James!

  • 02/2022, my monograph on learning with noisy labels has been accepted by MIT Press.

  • 01/2022, I will be serving as an Action Editor of TMLR.

  • 12/2021, I received the Faculty Early Career Research Excellence Award, University of Sydney.

  • 12/2021, I accepted the invitation to serve as an Area Chair for ICML 2022.

  • 11/2021, I accepted the invitation to serve as an Area Chair for UAI 2022.

  • 07/2021, I accepted the invitation to serve as an Area Chair for AAAI 2022.

  • 06/2021, I accepted the invitation to serve as an Area Chair for ICLR 2022.

  • 04/2021, we are organising a speical issue at the Machine Learning Journal.

  • 03/2021, I accepted the invitation to serve as an Area Chair for NeurIPS 2021.

  • 02/2021, we are organising the first Australia-Japan Workshop on Machine Learning.

  • 9/2020, I was named in the Early Achievers Leaderboard by The Australian.

  • 8/2020, I accepted the invitation to serve as an Area Chair for IJCAI 2021.

See more previous news here.


Selected Publications on Adversarial Learning

  • Modeling Adversarial Noise for Adversarial Defense. [PDF] [CODE]
    D. Zhou, N. Wang, B. Han, and T. Liu.
    In ICML, 2022.

  • Improving Adversarial Robustness via Mutual Information Estimation. [PDF]
    D. Zhou, N. Wang, X. Gao, B. Han, X. Wang, Y. Zhan, and T. Liu.
    In ICML, 2022.

  • Understanding Robust Overfitting of Adversarial Training and Beyond. [PDF] [CODE]
    C. Yu, B. Han, L. Shen, J. Yu, C. Gong, M. Gong, and T. Liu.
    In ICML, 2022.

  • Adversarial Robustness Through the Lens of Causality. [PDF] [CODE]
    Y. Zhang, M. Gong, T. Liu, G. Niu, X. Tian, B. Han, B. Schölkopf, and K. Zhang
    In ICLR, 2022.

  • Removing Adversarial Noise in Class Activation Feature Space [PDF] [CODE]
    D. Zhou, N. Wang, C. Peng, X. Gao, X. Wang, J. Yu, T. Liu
    In ICCV, 2021.

  • Towards Defending against Adversarial Examples via Attack-Invariant Features [PDF] [CODE]
    D. Zhou, T. Liu, B. Han, N. Wang, C. Peng, and X. Gao
    In ICML, 2021.

  • Probabilistic Margins for Instance Reweighting in Adversarial Training. [PDF] [CODE]
    Q. Wang, F. Liu, B. Han, T. Liu, C. Gong, G. Niu, M. Zhou, and M. Sugiyama.
    In NeurIPS, 2021.

  • Maximum Mean Discrepancy is Aware of Adversarial Attacks [PDF] [CODE]
    R. Gao, F. Liu, J. Zhang, B. Han, T. Liu, G. Niu, M. Sugiyama
    In ICML, 2021.

  • Learning Diverse-Structured Networks for Adversarial Robustness [PDF] [CODE]
    X. Du, J. Zhang, B. Han, T. Liu, Y. Rong, G. Niu, J. Huang, and M. Sugiyama
    In ICML, 2021.

  • Dual-Path Distillation: A Unified Framework to Improve Black-Box Attacks. [PDF]
    Y. Zhang, Y. Li, T. Liu, and X. Tian.
    In ICML, 2020.

Selected Publications on Learning with Noisy Labels

  • Estimating Instance-dependent Bayes-label Transition Matrix using a Deep Neural Network. [PDF] [CODE]
    S. Yang, E. Yang, B. Han, Y. Liu, M. Xu, G. Niu, and T. Liu.
    In ICML, 2022.

  • Selective-Supervised Contrastive Learning with Noisy Labels. [PDF] [CODE]
    S. Li, X. Xia, S. Ge, and T. Liu.
    In CVPR, 2022.

  • Instance-Dependent Label-Noise Learning With Manifold-Regularized Transition Matrix Estimation. [PDF] [CODE]
    D. Cheng, T. Liu, Y. Ning, N. Wang, B. Han, G. Niu, X. Gao, and M. Sugiyama.
    In CVPR, 2022.

  • Rethinking Class-Prior Estimation for Positive-Unlabeled Learning. [PDF] [CODE]
    Y. Yao, T. Liu, B. Han, M. Gong, G. Niu, M. Sugiyama, and Dacheng Tao
    In ICLR, 2022.

  • Sample Selection with Uncertainty of Losses for Learning with Noisy Labels. [PDF] [CODE]
    X. Xia, T. Liu, B. Han, M. Gong, J. Yu, G. Niu, and M. Sugiyama
    In ICLR, 2022.

  • Me-Momentum: Extracting Hard Confident Examples from Noisily Labeled Data [PDF] [CODE] [Oral]
    Y. Bai and T. Liu
    In ICCV, 2021.

  • Instance-Dependent Label-Noise Learning under Structural Causal Models. [PDF] [CODE]
    Y. Yao, T. Liu, M. Gong, B. Han, G. Niu, and K. Zhang.
    In NeurIPS, 2021.

  • Understanding and Improving Early Stopping for Learning with Noisy Labels. [PDF] [CODE]
    Y, Bai, E. Yang, B. Han, Y. Yang, J. Li, Y. Mao, G. Niu, and T. Liu.
    In NeurIPS, 2021.

  • Provably End-to-end Label-noise Learning without Anchor Points [PDF] [CODE]
    X. Li, T. Liu, B. Han, G. Niu, and M. Sugiyama
    In ICML, 2021.

  • Class2Simi: A Noise Reduction Perspective on Learning with Noisy Labels [PDF] [CODE]
    S. Wu*, X. Xia*, T. Liu, B. Han, M. Gong, N. Wang, H. Liu, and G. Niu
    In ICML, 2021.

  • A Second-Order Approach to Learning with Instance-Dependent Label Noise. [PDF] [CODE] [Oral]
    Z. Zhu, T. Liu, and Y. Liu.
    In CVPR, 2021.

  • Robust early-learning: Hindering the memorization of noisy labels. [PDF] [CODE]
    X. Xia, T. Liu, B. Han, C. Gong, N. Wang, Z. Ge, and Y. Chang.
    In ICLR, 2021.

  • Part-dependent Label Noise: Towards Instance-dependent Label Noise. [PDF] [CODE] [Spotlight]
    X. Xia, T. Liu, B. Han, N. Wang, M. Gong, H. Liu, G. Niu, D. Tao, and M. Sugiyama.
    In NeurIPS, 2020.

  • Dual T: Reducing Estimation Error for Transition Matrix in Label-noise Learning. [PDF] [CODE]
    Y. Yao, T. Liu, B. Han, M. Gong, J. Deng, G. Niu, and M. Sugiyama.
    In NeurIPS, 2020.

  • Learning with Bounded Instance- and Label-dependent Label Noise. [PDF] [CODE]
    J. Cheng, T. Liu, K. Rao, and D. Tao.
    In ICML, 2020.

  • Are Anchor Points Really Indispensable in Label-Noise Learning? [PDF] [CODE]
    X. Xia, T. Liu, N. Wang, B. Han, C. Gong, G. Niu, and M. Sugiyama.
    In NeurIPS, 2019.

  • Learning with Biased Complementary Labels. [PDF] [CODE] [Oral]
    X. Yu, T. Liu, M. Gong, and D. Tao.
    In ECCV, 2018.

  • Classification with Noisy Labels by Importance Reweighting. [PDF] [CODE]
    T. Liu and D. Tao.
    IEEE T-PAMI, 38(3): 447-461, 2015.

Selected Publications on Transfer Learning

  • Confident-Anchor-Induced Multi-Source-Free Domain Adaptation. [PDF] [CODE]
    J. Dong, Z. Fang, A. Liu, G. Sun, and T. Liu.
    In NeurIPS, 2021.

  • Domain Generalization via Entropy Regularization. [PDF] [CODE]
    S. Zhao, M. Gong, T. Liu, H. Fu, and D. Tao.
    In NeurIPS, 2020.

  • Transferring Knowledge Fragments for Learning Distance Metric from A Heterogeneous Domain. [Paper] [CODE]
    Y. Luo, Y. Wen, T. Liu, and D. Tao.
    IEEE T-PAMI, 41(4): 1013-1026, 2019.

  • LTF: A Label Transformation Framework for Correcting Label Shift. [PDF] [CODE]
    J. Guo, M. Gong, T. Liu, K. Zhang, and D. Tao.
    In ICML, 2020.

  • Deep Domain Generalization via Conditional Invariant Adversarial Networks. [PDF] [CODE]
    Y. Li, X. Tian, M. Gong, Y. Liu, T. Liu, K. Zhang, and D. Tao.
    In ECCV, 2018.

  • Understanding How Feature Structure Transfers in Transfer Learning. [PDF]
    T. Liu, Q. Yang, and D. Tao.
    In IJCAI, 2017.

  • Domain Adaptation with Conditional Transferable Components. [PDF] [CODE]
    M. Gong, K. Zhang, T. Liu, D. Tao, C. Glymour, and B. Schölkopf.
    In ICML, 2106.

Selected Publications on Statistical (Deep) Learning Theory

  • On the Rates of Convergence from Surrogate Risk Minimizers to the Bayes Optimal Classifier. [PDF]
    J. Zhang, T. Liu, and D. Tao.
    IEEE T-NNLS, accepted 2021.

  • Control Batch Size and Learning Rate to Generalize Well: Theoretical and Empirical Evidence. [PDF]
    F. He, T. Liu, and D. Tao.
    In NeurIPS, 2019.

  • Algorithmic Stability and Hypothesis Complexity. [PDF]
    T. Liu, G. Lugosi, G. Neu and D. Tao.
    In ICML , 2017.

  • Algorithm-Dependent Generalization Bounds for Multi-Task Learning. [Paper]
    T. Liu, D. Tao, M. Song, and S. J. Maybank.
    IEEE T-PAMI, 39(2): 227-241, 2017.

See more publications here.


Sponsors

Australian Research Council Usyd CVI CPA Meituan NSSN InteliCare InteliCare InteliCare InteliCare